Support vector machine for image based automated inspection system

2004 ◽  
Author(s):  
Chien-Ting Kuo ◽  
Chih-Hsien Kung ◽  
Chih-Ming Kung ◽  
J.H. Jeng
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Inspection System ◽  
Automated Inspection

Keyboard Characters Recognition with Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 2283-2287
Author(s):  
Xin Wen Gao ◽  
Xing Jian Guan ◽  
Ben Bo Guan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Dimensional Space ◽  
Training Data ◽  
Support Vector ◽  
Inspection System ◽  
Higher Dimensional ◽  
Key Technologies ◽  
Recognition Function ◽  
The Difference

This paper proposed a method to detect the defects of keyboard characters. The work, which is a part of the keyboard inspection system, integrates two key technologies to realize the recognition function. First, Feature extraction is applied to select the best properties of the keyboard characters to distinguish the difference and six features are chosen. Second, we integrate support vector machine (SVM) into the classification method and the radial basis kernel function is used to map the training data into higher dimensional space to facilitate the classification. We get a satisfied result in the classification finally which demonstrate the proposed approach is effective.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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